Toward Precision Psychiatry: Using Clinical and Pharmacokinetic Data to Increase Antidepressant Dose or Change...

Decision trees can use clinical predictors to determine whether to continue the same antidepressant or switch to a different treatment in older patients with major depressive disorder (MDD). We examined whether pharmacogenetic and pharmacokinetic variables could improve their performance.

We analyzed 191 participants from the Incomplete Response in Late-Life Depression: Getting to Remission (IRL-Grey) trial who had not responded fully after 4 weeks of venlafaxine XR (150 mg/d) and for whom venlafaxine up to 300 mg/d was continued for 8 additional weeks. CYP2D6 genotypes were determined; venlafaxine, o-desmethylvenlafaxine (ODV), and active moiety (AM) exposures at week 4 were calculated using population pharmacokinetic modeling. Decision tree analysis was performed using 5 early clinical predictors of eventual nonresponse identified in previous research and 4 pharmacogenetic and pharmacokinetic potential predictors. One decision tree was designed to optimize specificity (k=0.3), and another to optimize sensitivity (k=0.7).

Longer episode duration and lack of partial response at week 4 were retained as clinical predictors, and lower AM and ODV exposures were identified as additional predictors. Negative predictive values (NPVs) of the high-specificity and high-sensitivity trees (77.7% and 73.0%, respectively) were similar to NPVs in trees based solely on clinical predictors.

Our methods can guide future studies combining clinical and biomarker data to address applied pharmacological questions relevant to day-to-day practice.